Job Title: Sr Ethernet AI Network Engineer Location: Remote – United States Engagement: Contract – 6 Months (1,000 Hours) Role Overview: Client is seeking a Sr Ethernet AI Network Engineer to provide senior network engineering services for Ethernet-based AI data center fabrics supporting GPU compute clusters, storage connectivity, and management plane services in support of a client's customer. The role requires strong operational judgment, hands-on troubleshooting, and the ability to resolve failures across spine-leaf architectures under production pressure. Key Responsibilities: Deploy, validate, and support Ethernet fabrics used for AI and HPC cluster environments. Troubleshoot L1 through L4 issues involving optics, transceivers, cabling, link bring-up, VLANs, MLAG, ECMP, BGP, underlay/overlay reachability, congestion, and packet loss. Validate network readiness for distributed training and large east-west traffic patterns. Diagnose performance issues related to buffer pressure, microbursts, PFC behavior, QoS policy, MTU mismatch, routing instability, and oversubscription. Work closely with Linux, storage, and cluster deployment teams to isolate host-versus-network fault domains. Review and execute change plans for switch provisioning, firmware upgrades, topology expansion, and maintenance events. Capture packet-level and counter-based evidence to drive root cause analysis. Build operational standards for cable mapping, port policy consistency, and fabric health validation. Required Qualifications: 7+ years in large-scale data center networking, including high-bandwidth Ethernet fabrics. Strong experience with spine-leaf design, routing, switching, and production troubleshooting. Hands-on skill with BGP, EVPN/VXLAN, MLAG, ECMP, QoS, PFC, RoCE considerations, and telemetry interpretation. Experience validating optics, breakout configurations, cable plant integrity, and port-level consistency. Proven ability to troubleshoot distributed application impact caused by network behavior. Comfort using switch CLI, automation tooling, and packet/counter analysis workflows. Strong documentation habits for topology, incident timelines, and remediation plans. Preferred Qualifications: Direct experience with AI fabrics carrying large-scale GPU collective traffic. Familiarity with SONiC, Cumulus Linux, or vendor NOS platforms used in AI data centers. Experience with streaming telemetry, Prometheus/Grafana, and network SRE operating models.